Images of fingerprints are routinely used to identity individuals. Because each person's fingerprints are unique, images of unknown fingerprints can be compared with images of known fingerprints stored in a database. For example, the database of fingerprints in the FBI archives includes over 30 million ten-print “cards.” When a matching set of fingerprints is found in the database, the identity of the person can be verified. Standard image formats have been adopted for recording and storing images of a person's fingerprints along with additional information such as: names, alias names, birth date, height, weight, hair color, eye color, race, and so forth.
Prior art fingerprint verification systems typically include techniques for locating, classifying, and identifying key features of fingerprints such as pattern type, ridge features and direction. Ridge features are defined by bifurcations and endings of ridge flows on a person's fingers. These minutiae include cores, deltas, whorls, loops, arches, tented arches, and the like. The minutiae data can include (x, y) coordinates of their locations and degree of orientations (q).
The well known Henry system is the predominant manual system used by law enforcement agencies for fingerprint identification. However, the Henry system uses a relatively small number of classifying characteristics. Hence the total number of identifier codes is too small to uniquely account for the millions of fingerprints in a comprehensive database.
Generally, three types of automated comparison methods are used for searching a fingerprint database. The first method uses a ten-print-to-ten-print comparison where each finger and its orientation is known. There, the set of prints is complete, and the quality of the images is good. The result of the first method is usually conclusive. The second method uses a latent-to-ten-print comparison. The latent print can be “lifted” from some arbitrary surface through a variety of known techniques. In contrast with the ten-print method, latent prints are usually partial and of poor quality. Often, the finger numbers and their orientations are also unknown. The third method uses a latent-to-latent comparison to determine if two separately obtained prints belong to the same person, even though the exact identity of the person may be unknown.
Generally, automated comparison is performed by first aligning an unknown “test” print with a known or “reference” print. Then, the relative spatial and angular position of comparable minutiae in the two prints are superimposed, evaluated and scored according to the number of minutiae that are common to the two prints. The method completes when the unknown print has been compared with all closely matching known prints. A high score indicated a larger number of common minutiae and a probable match. Typically, each pair of matching minutiae increases the score by one. Some typical prior matching methods are described in National Bureau of Standards (NBS) Technical Notes 538 and 878, and NBS Special Publication 500-89.
Prior art fingerprint comparison systems only work well with uniform, high quality images of a ten-print set. However, images of latent prints are often partial and low contrast, making the systems unreliable and inconsistent. In addition, fingerprint artifacts such as cuts, scrapes, abrasions, and scars can lead to “false” minutiae, such as breaks, islands, short branches, lakes, and joins. False minutiae cause identification failures and necessitate operator intervention, which increases cost and reduces throughput.
Sasakawa et al., in “Personal Verification System with High Tolerance of Poor Quality Images,” SPIE Vol. 1386, Machine Vision Systems, pp. 265-272, 1990. describe a fingerprint verification system (FVS) that uses image enhancement techniques, such as a directional spatial filter and local thresholding, to extract minutiae data. They use both coarse and fine matching for minutiae data, see also, U.S. Pat. No. 6,229,922 “Method and Apparatus for Comparing Incoming Data with Registered Data” issued to Sasakawa et al. on May 8, 2001. Their similarity score is based on a normalized integer count of the number of matching minutiae. Their method scores all matching minutiae equally. That can lead to false acceptances and false rejections, particularly when similar minutiae are located near each other.
Therefore, there is a need for a fingerprint comparison method that can better discriminate features in images.